Building on the successes of generative adversarial nets [1]},
conditional GANs [2]},[3]} incorporate a specific conditional restriction
in their generator network, so that it learns to generate fake samples under that condition.
Conditional GAN incorporate a binary mask or label as conditional input by concatenating it with the input image or with a latent noise vector.
